Human motion capture (MOCAP) systems are vital while determining the loads occurring at the joints. Most of the clinical MOCAP systems are very costly, requiring investment and infrastructure. Therefore, alternative technologies are in demand. In this study, a novel markerless wearable MOCAP system was assessed for its compatibility with a biomechanical modeling software. To collect evidence, experiments were designed in two stages for quantifying the range of motion (ROM) of the hip joint, in vitro and in vivo. Three constrained single-plane motions—abduction/adduction, flexion/extension, and internal/external rotation movements of the active leg—were analyzed. The data were collected from 14 healthy volunteers, using the wearable system and a medical grade optoelectronic MOCAP system simultaneously and compared against. For the in vitro study, the root-mean-square error (RMSE) for the abduction/adduction motion of the hip joint was calculated as 0.11 deg/0.30 deg and 0.11 deg/0.09 deg, respectively, for the wearable and the opto-electronic system. The in vivo Bland–Altman plots showed that the two system data are comparable. The simulation software is found compatible to run the simulations in offline mode. The wearable system could be utilized in the field of biomechanics software for running the kinetic simulations. The results demonstrated that the wearable system could be an alternative in the field of biomechanics based on the evidence collected.

Background: Previous thorax models have been proposed for gait analysis, however these require markers to be placed on the back. This presents a limitation in the kinematic analysis of the thorax under load carriage conditions. Research question: This study evaluated the validity and reliability of a thorax marker set that does not require markers to be placed on the back (HubemaLab model) when compared to 3 previously published marker set models. Methods: 17 young adults were evaluated while walking at their self-selected speed. A 12 camera motion capture system was used to acquire the marker position data which was then processed using the respective models using Visual-3D. The level of agreement for the flexion/extension peak, right/left lateral peak and right/left rotation peak of the thorax angle and angular velocity; together with the range of motion and thorax angular velocities in the three planes was found between each thorax marker set, while the reliability was measured using the intraclass correlation coefficient. Results: The ICC results for the thorax angle ROM and the range of thorax angular velocity between the HubemaLab model and the other models showed excellent to good reliability in all three planes. While the ICCs for the peak flexion/extension, peak right/left lateral flexion and peak right/left rotation showed excellent to moderate reliability in all three planes. Conclusion: The new model could be potentially valuable for kinematic gait analysis under load carriage conditions which obscure markers placed on the back.

Evidence on epimuscular myofascial force transmission (EMFT) was shown for undissected muscle in situ. We hypothesize that global length changes of gastrocnemius muscle-tendon complex in vivo will cause sizable and heterogeneous local strains within all muscles of the human lower leg. Our goal is to test this hypothesis. A method was developed and validated using high-resolution 3D magnetic resonance image sets and Demons nonrigid registration algorithm for performing large deformation analyses. Calculation of strain tensors per voxel in human muscles in vivo allowed quantifying local heterogeneous tissue deformations and volume changes. After hip and knee movement (Δ knee angle ≈ 25 deg) but without any ankle movement, local lengthening within m. gastrocnemius was shown to occur simultaneously with local shortening (maximally by +34.2% and −32.6%, respectively) at different locations. Moreover, similar local strains occur also within other muscles, despite being kept at constant muscle-tendon complex length. This is shown for synergistic m. soleus and deep flexors, as well as for antagonistic anterior crural and peroneal muscle groups: minimum peak lengthening and shortening equaled 23.3% and 25.54%, respectively despite global isometric conditions. These findings confirm our hypothesis and show that in vivo, muscles are in principle not independent mechanically.

This study presents a methodology to determine thumb and index finger kinematics while utilizing a minimal set of markers. The motion capture of skin-surface markers presents inherent challenges for the accurate and comprehensive measurement of digit kinematics. As such, it is desirable to utilize robust methods for assessing digit kinematics with fewer markers. The approach presented in this study involved coordinate system alignment, locating joint centers of rotation, and a solution model to estimate three-dimensional (3-D) digit kinematics. The solution model for each digit was based on assumptions of rigid-body interactions, specific degrees of freedom (DOFs) at each located joint, and the aligned coordinate system definitions. Techniques of inverse kinematics and optimization were applied to calculate the 3-D position and orientation of digit segments during pinching between the thumb and index finger. The 3-D joint center locations were reliably fitted with mean coefficients of variation below 5%. A parameterized form of the solution model yielded feasible solutions that met specified tolerance and convergence criteria for over 85% of the test points. The solution results were intuitive to the pinching function. The thumb was measured to be rotated about the CMC joint to bring it into opposition to the index finger and larger rotational excursions (>10 deg) were observed in flexion/extension compared to abduction/adduction and axial rotation for all joints. While the solution model produced results similar to those computed from a full marker set, the model facilitated the usage of fewer markers, which inherently lessened the effects of passive motion error and reduced the post-experimental effort required for marker processing.

Background: The objectives of this study were to characterize the translational and rotational accuracy of a model-based tracking technique for quantifying elbow kinematics and to demonstrate its in vivo application. Method of Approach: The accuracy of a model-based tracking technique for quantifying elbow kinematics was determined in an in vitro experiment. Biplane X-ray images of a cadaveric elbow were acquired as it was manually moved through flexion-extension. The 3D position and orientation of each bone was determined using model-based tracking. For comparison, the position and orientation of each bone was also determined by tracking the position of implanted beads with dynamic radiostereometric analysis. Translations and rotations were calculated for both the ulnohumeral and radiohumeral joints, and compared between measurement techniques. To demonstrate the in vivo application of this technique, biplane X-ray images were acquired as a human subject extended their elbow from full flexion to full extension. Results: The in vitro validation demonstrated that the model-based tracking technique is capable of accurately measuring elbow motion, with reported errors averaging less than ±1.0 mm and ±1.0 deg. For the in vivo application, the carrying angle changed from an 8.3 ± 0.5 deg varus position in full flexion to an 8.4 ± 0.5 deg valgus position in full extension. Conclusions: Model-based tracking is an accurate technique for measuring in vivo, 3D, dynamic elbow motion. It is anticipated that this experimental approach will enhance our understanding of elbow motion under normal and pathologic conditions.

Shoe-surface interface characteristics have been implicated in the high incidence of ankle injuries suffered by athletes. Yet, the differences in rotational stiffness among shoes may also influence injury risk. It was hypothesized that shoes with different rotational stiffness will generate different patterns of ankle ligament strain. Four football shoe designs were tested and compared in terms of rotational stiffness. Twelve (six pairs) male cadaveric lower extremity limbs were externally rotated 30 deg using two selected football shoe designs, i.e., a flexible shoe and a rigid shoe. Motion capture was performed to track the movement of the talus with a reflective marker array screwed into the bone. A computational ankle model was utilized to input talus motions for the estimation of ankle ligament strains. At 30 deg of rotation, the rigid shoe generated higher ankle joint torque at 46.2 ± 9.3 Nm than the flexible shoe at 35.4 ± 5.7 Nm. While talus rotation was greater in the rigid shoe (15.9 ± 1.6 deg versus 12.1 ± 1.0 deg), the flexible shoe generated more talus eversion (5.6 ± 1.5 deg versus 1.2± 0.8 deg). While these talus motions resulted in the same level of anterior deltoid ligament strain (approxiamtely 5%) between shoes, there was a significant increase of anterior tibiofibular ligament strain (4.5± 0.4% versus 2.3 ± 0.3%) for the flexible versus more rigid shoe design. The flexible shoe may provide less restraint to the subtalar and transverse tarsal joints, resulting in more eversion but less axial rotation of the talus during foot/shoe rotation. The increase of strain in the anterior tibiofibular ligament may have been largely due to the increased level of talus eversion documented for the flexible shoe. There may be a direct correlation of ankle joint torque with axial talus rotation, and an inverse relationship between torque and talus eversion. The study may provide some insight into relationships between shoe design and ankle ligament strain patterns. In future studies, these data may be useful in characterizing shoe design parameters and balancing potential ankle injury risks with player performance.

Skin marker-based motion analysis has been widely used in biomechanical studies and clinical applications. Unfortunately, the accuracy of knee joint secondary motions is largely limited by the nonrigidity nature of human body segments. Numerous studies have investigated the characteristics of soft tissue movement. Utilizing these characteristics, we may improve the accuracy of knee joint motion measurement. An optimizer was developed by incorporating the soft tissue movement patterns at special bony landmarks into constraint functions. Bony landmark constraints were assigned to the skin markers at femur epicondyles, tibial plateau edges, and tibial tuberosity in a motion analysis algorithm by limiting their allowed position space relative to the underlying bone. The rotation matrix was represented by quaternion, and the constrained optimization problem was solved by Fletcher’s version of the Levenberg–Marquardt optimization technique. The algorithm was validated by using motion data from both skin-based markers and bone-mounted markers attached to fresh cadavers. By comparing the results with the ground truth bone motion generated from the bone-mounted markers, the new algorithm had a significantly higher accuracy (root-mean-square (RMS) error: 0.7 ± 0.1 deg in axial rotation and 0.4 ± 0.1 deg in varus-valgus) in estimating the knee joint secondary rotations than algorithms without bony landmark constraints (RMS error: 1.7 ± 0.4 deg in axial rotation and 0.7 ± 0.1 deg in varus-valgus). Also, it predicts a more accurate medial-lateral translation (RMS error: 0.4 ± 0.1 mm ) than the conventional techniques (RMS error: 1.2 ± 0.2 mm ). The new algorithm, using bony landmark constrains, estimates more accurate secondary rotations and medial-lateral translation of the underlying bone.

The aim of skin-marker-based motion analysis is to reconstruct the motion of a kinematical model from noisy measured motion of skin markers. Existing kinematic models for reconstruction of chains of segments can be divided into two categories: analytical methods that do not take joint constraints into account and numerical global optimization methods that do take joint constraints into account but require numerical optimization of a large number of degrees of freedom, especially when the number of segments increases. In this study, a new and largely analytical method for a chain of rigid bodies is presented, interconnected in spherical joints (chain-method). In this method, the number of generalized coordinates to be determined through numerical optimization is three, irrespective of the number of segments. This new method is compared with the analytical method of Veldpaus et al. [1988, “A Least-Squares Algorithm for the Equiform Transformation From Spatial Marker Co-Ordinates,” J. Biomech., 21, pp. 45–54] (Veldpaus-method, a method of the first category) and the numerical global optimization method of Lu and O’Connor [1999, “Bone Position Estimation From Skin-Marker Co-Ordinates Using Global Optimization With Joint Constraints,” J. Biomech., 32, pp. 129–134] (Lu-method, a method of the second category) regarding the effects of continuous noise simulating skin movement artifacts and regarding systematic errors in joint constraints. The study is based on simulated data to allow a comparison of the results of the different algorithms with true (noise- and error-free) marker locations. Results indicate a clear trend that accuracy for the chain-method is higher than the Veldpaus-method and similar to the Lu-method. Because large parts of the equations in the chain-method can be solved analytically, the speed of convergence in this method is substantially higher than in the Lu-method. With only three segments, the average number of required iterations with the chain-method is 3.0 ± 0.2 times lower than with the Lu-method when skin movement artifacts are simulated by applying a continuous noise model. When simulating systematic errors in joint constraints, the number of iterations for the chain-method was almost a factor 5 lower than the number of iterations for the Lu-method. However, the Lu-method performs slightly better than the chain-method. The RMSD value between the reconstructed and actual marker positions is approximately 57% of the systematic error on the joint center positions for the Lu-method compared with 59% for the chain-method.

The aim of this work was the design and evaluation of a computational model to predict the functional behavior of the lower leg and foot/ankle complex whereby joint behavior was dictated by three-dimensional articular contact, ligamentous constraints, muscle loading, and external perturbation. Three-dimensional bony anatomy was generated from stacked CT images after which ligament mimicking elements were attached and muscle/body loading added to recreate the experimental conditions of selected cadaveric studies. Comparisons of model predictions to results from two different experimental studies were performed for the function of the medial arch in weight bearing stance and the contributions of soft tissue structures to arch stability. Sensitivity simulations evaluated selected in situ strain and stiffness values for ligament tissue. The greatest contributor to arch stability was the plantar fascia, which provided 79.5% of the resistance to arch collapse, followed by the plantar ligaments (12.5%), and finally the spring ligament (8.0%). Strains measured after plantar fasciotomy increased in the remaining plantar ligament by ∼ 300 % and spring ligament by ∼ 200 % . Sensitivity tests varying both in situ strain and stiffness across reported standard deviations showed that functional trends remained the same and true to experimental data, although absolute magnitudes changed. While not measured experimentally, the model also predicted that load can increase dramatically in the remaining plantar tissues when one of such tissues is removed. Overall, computational predictions of stability and soft tissue load sharing compared well with experimental findings. The strength of this simulation approach lies in its capacity to predict biomechanical behavior of modeled structures and to capture physical parameters of interest not measurable in experimental simulations or in vivo.

The tibio-femoral joint has been mechanically approximated with two fixed kinematic axes of rotation, the longitudinal rotational (LR) axis in the tibia and the flexion-extension (FE) axis in the femur. The mechanical axis finder developed by Hollister et al. (1993, “The Axes of Rotation of the Knee,” Clin. Orthop. Relat. Res., 290, pp. 259–268 ) identified the two fixed axes but the visual-based alignment introduced errors in the method. Therefore, the objectives were to develop and validate a new axis finding method to identify the LR and FE axes which improves on the error of the mechanical axis finder. The virtual axis finder retained the concepts of the mechanical axis finder but utilized a mathematical optimization to identify the axes. Thus, the axes are identified in a two-step process: First, the LR axis is identified from pure internal-external rotation of the tibia and the FE axis is identified after the LR axis is known. The validation used virtual simulations of 3D video-based motion analysis to create relative motion between the femur and tibia during pure internal-external rotation, and flexion-extension with coupled internal-external rotation. The simulations modeled tibio-femoral joint kinematics and incorporated 1 mm of random measurement error. The root mean squared errors (RMSEs) in identifying the position and orientation of the LR and FE axes with the virtual axis finder were 0.45 mm and 0.20 deg, and 0.11 mm and 0.20 deg, respectively. These errors are at least two times better in position and seven times better in orientation than those of the mechanical axis finder. Variables, which were considered a potential source of variation between joints and/or measurement systems, were tested for their sensitivity to the RMSE of identifying the axes. Changes in either the position or orientation of a rotational axis resulted in high sensitivity to translational RMSE (6.8 mm of RMSE per mm of translation) and rotational RMSE (1.38 deg of RMSE per degree of rotation), respectively. Notwithstanding these high sensitivities, corresponding errors can be reduced by segmenting the range of motion into regions where changes in either position or orientation are small. The virtual axis finder successfully increased the accuracy of the mechanical axis finder when the axes of motion are fixed with respect to the bones, but must be used judiciously in applications which do not have fixed axes of rotation.

When using optical motion capture systems, increasing the number of cameras improves the visibility. However, the software used to deal with the information fusion from multiple cameras may compromise the accuracy of the system due to camera dropout, which can vary with time. In cadaver studies of radial head motion, increasing the number of cameras used by the motion capture system seemed to decrease the accuracy of the measurements. This study investigates the cause. The hypothesis was that errors in position can be induced when markers are obscured from and then restored to a camera’s viewable range, as can happen in biomechanical studies. Accuracy studies quantified the capabilities of the motion capture system with precision translation and rotation movements. To illustrate the effect that abrupt perceived changes in a marker’s position can have on the calculation of radial head travel, simulated motion experiments were performed. In these studies, random noise was added to simulated data, which obscured the resultant path of motion. Finally, camera-blocking experiments were performed in which precise movements were measured with a six-camera Vicon system and the errors between the actual and perceived motion were computed. During measurement, cameras were selectively blocked and restored to view. The maximum errors in translation and rotation were 3.7 mm and 0.837 deg, respectively. Repeated measures analysis of variance (ANOVAs) ( α = 0.05 ) confirmed that the camera-blocking influenced the results. Taken together, these results indicate that camera-switching can affect the observation of fine movements using a motion analysis system with a large number of cameras. One solution is to offer opportunity for user interaction in the software to choose the cameras used for each instant of time.

Dynamic assessment of three-dimensional (3D) skeletal kinematics is essential for understanding normal joint function as well as the effects of injury or disease. This paper presents a novel technique for measuring in-vivo skeletal kinematics that combines data collected from high-speed biplane radiography and static computed tomography (CT). The goals of the present study were to demonstrate that highly precise measurements can be obtained during dynamic movement studies employing high frame-rate biplane video-radiography, to develop a method for expressing joint kinematics in an anatomically relevant coordinate system and to demonstrate the application of this technique by calculating canine tibio-femoral kinematics during dynamic motion. The method consists of four components: the generation and acquisition of high frame rate biplane radiographs, identification and 3D tracking of implanted bone markers, CT-based coordinate system determination, and kinematic analysis routines for determining joint motion in anatomically based coordinates. Results from dynamic tracking of markers inserted in a phantom object showed the system bias was insignificant (−0.02 mm). The average precision in tracking implanted markers in-vivo was 0.064 mm for the distance between markers and 0.31° for the angles between markers. Across-trial standard deviations for tibio-femoral translations were similar for all three motion directions, averaging 0.14 mm (range 0.08 to 0.20 mm). Variability in tibio-femoral rotations was more dependent on rotation axis, with across-trial standard deviations averaging 1.71° for flexion/extension, 0.90° for internal/external rotation, and 0.40° for varus/valgus rotation. Advantages of this technique over traditional motion analysis methods include the elimination of skin motion artifacts, improved tracking precision and the ability to present results in a consistent anatomical reference frame.